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This study aims to explore the conversion of metaverse marketing (MVM) into strategic agility among SMEs based on dynamic capabilities (DC) and dynamic management capabilities (DMC) theories. This paper discusses how constructs such as immersive marketing technologies (IMT), customer immersion (CI) and managerial capabilities (MC) play critical role in the transformation of MVM into strategic agility (SA).

A theoretical framework based on DC and DMC theories, and a comprehensive review of the literature on MVM, IMT, CI, MC and SA, was developed in order to theoretically investigate the relationships between MVM and SA. In this theoretical framework, MVM is the independent variable, while the dependent variable is SA. Also, IMT and CI both mediate the association between MVM and SA, while MC moderate the association between MVM and SA in one stream; and CI and SA in another stream.

This research study develops a theoretical framework that recommends nine set of important research propositions in MVM. An extensive literature review was conducted to examine the theoretical framework on the effect of MVM on SA. The proposed theoretical framework suggests that brand community development and communication, experiential marketing and personalisation in MVM, once accessed through IMT (i.e. VR, AR, MR) and CI (i.e. customer engagement, customer absorption-customer acquisition and assimilation of knowledge, presence) can produce significant SA through customer experience management, value co-creation and process innovation.

This current study develops a theoretical framework that theorise the relationship between MVM and SA rooted in literature on MVM and SA, and also based on DC and DMC perspective. The moderating effect of MC on the relationship between IMT and SA on one hand, and CI and SA on the other, provides support to IMT and CI as mediators in the transformation of MVM into SA. This study also provides insight into SME adoption of MVM and how it generates SA. Lastly, the current study contributes to the body of knowledge on MVM, IMT, CI, MC and SA.

The aim of this paper is to provide a narrative review of previous research on tourism demand modelling and forecasting and potential future developments.

A narrative approach is taken in this review of the current body of knowledge.

Significant methodological advancements in tourism demand modelling and forecasting over the past two decades are identified.

The distinct characteristics of the various methods applied in the field are summarised and a research agenda for future investigations is proposed.

本文旨在对先前关于旅游需求建模和预测的研究进行叙述性回顾并对未来潜在发展进行展望。

本文采用叙述性回顾方法对当前知识体系进行了评论。

本文确认了过去二十年旅游需求建模和预测方法论方面的重要进展。

本文总结了该领域应用的各种方法的独特特征, 并对未来研究提出了建议。

El objetivo de este documento es ofrecer una revisión narrativa de la investigación previa sobre modelización y previsión de la demanda turística y los posibles desarrollos futuros.

En esta revisión del marco actual de conocimientos sobre modelización y previsión de la demanda turística y los posibles desarrollos futuros,se adopta un enfoque narrativo.

Se identifican avances metodológicos significativos en la modelización y previsión de la demanda turística en las dos últimas décadas.

Se resumen las características propias de los diversos métodos aplicados en este campo y se propone una agenda de investigación para futuros trabajos.

This paper reviews recent research on the expected economic effects of developing artificial intelligence (AI) through a survey of the latest publications, in particular papers and reports issued by academics, consulting companies and think tanks.

Our paper represents a point of view on AI and its impact on the global economy. It represents a descriptive analysis of the AI phenomenon.

AI represents a driver of productivity and economic growth. It can increase efficiency and significantly improve the decision-making process by analyzing large amounts of data, yet at the same time it creates equally serious risks of job market polarization, rising inequality, structural unemployment and the emergence of new undesirable industrial structures.

This paper presents itself as a building block for further research by introducing the two main factors in the production function (Cobb-Douglas): labor and capital. Indeed, Zeira (1998) and Aghion, Jones and Jones (2017) suggested that AI can stimulate growth by replacing labor, which is a limited resource, with capital, an unlimited resource, both for the production of goods, services and ideas.

Our study contributes to the previous literature and presents a descriptive analysis of the impact of AI on technological development, economic growth and employment.

Extreme weather events are on the rise around the globe. Nevertheless, it is unclear how these extreme weather events have impacted the supply chain sustainability (SCS) framework. To this end, this paper aims to identify and analyze the aspects and criteria to enable manufacturing firms to navigate shifts toward SCS under extreme weather events.

The Best-Worst Method is deployed and extended with the entropy concept to obtain the degree of significance of the identified framework of aspects and criteria for SCS in the context of extreme weather events through the lens of managers in the manufacturing firms of a developing country-Nigeria.

The results show that extreme weather preparedness and economic aspects take center stage and are most critical for overcoming the risk of unsustainable patterns within manufacturing supply chains under extreme weather events in developing country.

This study advances the body of knowledge by identifying how extreme weather events have become a significant moderator of the SCS framework in manufacturing firms. This research will assist decision-makers in the manufacturing sector to position viable niche regimes to achieve SCS in the context of extreme weather events for expected performance gains.

This study aims to price the risk contribution of general Takaful at the level of tariff cells, considering a spatial dependency framework.

Three different models, including a generalized linear model, a generalized linear mixed model (GLMM) and a spatial generalized linear mixed model (SGLMM), according to the actuarial modeling of general Takaful, are used to price pure risk contribution (PRC).

The results reveal that the SGLMM yields more accurate predictions of the PRC compared to the other models, emphasizing the significance of spatial modeling in this context. Following the estimation of the PRC, the gross contribution according to the mechanism of Takaful models is calculated considering the spatial model.

Considering the similarities between Takaful and insurance, this study addresses the pricing of general Takaful within different Takaful models through a spatial dependency framework, such that the practical implications of the study are applicable for running Takaful's business in both Islamic and non-Islamic countries.

Most studies consider only the social or practical view of Takaful. This study contributes to the broader knowledge and understanding of Takaful by presenting a conceptual understanding of Takaful and then investigates the practical application of pricing risk contribution using innovative modeling of claim frequency and severity at the level of tariff cells.

This paper aims to measure and unveil the relationship between customer satisfaction and efficiency levels in the most relevant Brazilian airports.

The authors utilize a two-stage network DEA (data envelopment analysis) and AHP (analytic hierarchy process) model as the cornerstones of the study. The first stage of the network productive structure focuses on examining the infrastructure efficiency of the selected airports, while the second stage assesses their business efficiency.

Although the results indicate that infrastructure and business efficiency levels are heterogeneous and widely dispersed across airports, controlling the regression results with different contextual variables suggests that the impact of efficiency levels on customer satisfaction is mediated by a set of socio-economic and demographic (endogenous) and regulatory (exogenous) variables. Furthermore, encouraging investment in airports is necessary to achieve higher infrastructural efficiency and scale efficiency, thereby improving customer satisfaction.

There is a scarcity of studies examining the relationships among customer satisfaction, privatization and airport efficiency, particularly in developing countries like Brazil.

The purpose of this study is to look at the factors that drive people to participate in the sharing economy (SE). Based on the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB), the study proposes a mathematical model. The study’s ultimate objective is to help businesses attract more involved customers and promote collaborative consumption as a sustainable alternative to typical consumption patterns. The study offers a conceptual framework established via a thorough literature review to examine Indian customers’ use behavior toward SE platforms. A one-sample two-tailed t-test is used to assess the framework’s efficacy. The research fills gap in the literature on the SE by investigating the factors that determine subjective norms (SN), attitudes (A), and perceived behavioral control (PBC). A framework is provided that takes behavioral intention (BI) contemplated as a mediating variable. The research improves TAM and TPB by including new factors such as technical characteristics. This research adds to the body of knowledge on the digital SE by underlining the relevance of usage behavior in comprehending Indian customers, where A, SN, and PBC are important aspects. The research presents a paradigm for better understanding customers’ attitudes and behaviors toward various SE platforms, which might help academics, practitioners, and policy makers situate their initiatives within the larger field of sharing. The study’s categorizations of Indian consumers’ A, SN, PBC, and BI toward the SE might potentially advise on future research and government policies.

This study aims to examine the predictive power of childhood adversity and severity of positive symptoms on suicidality, controlling for selected sociodemographics factors, among hospitalized patients diagnosed with schizophrenia in Jordan.

This study used a descriptive-explorative design. The study was conducted at two major psychiatric hospitals in Jordan. The targeted sample was 66 patients diagnosed with schizophrenia. Data was collected using a structured format in the period February–April 2024.

A two-step multiple hierarchical regression analysis was conducted. In the first model, childhood adversity and the severity of positive symptoms were entered. In the second model, sociodemographic variables were entered. The analysis revealed that the first model (F = 5.35, p = 0.007) was statistically significant. The second model (F = 717, p < 0.001) was statistically significant. Furthermore, the analysis revealed that childhood adversity was not a significant predictor for suicidality. However, positive symptoms and patients’ demographics (age, number of hospitalizations and length of being diagnosed with schizophrenia) were significant predictors of suicidality. The analysis revealed that childhood adversity was not a significant predictor of suicidality. However, positive symptoms and patients’ demographics (age, number of hospitalizations and length of being diagnosed with schizophrenia) were significant predictors of suicidality.

One limitation of this study is related to the sample and the setting where there were only 66 patients recruited from governmental hospitals within inpatient wards. Thus, the upcoming studies should include more participants from private hospitals and different hospital settings including outpatient and emergency departments.

The research provides empirical insights that positive symptoms, age hospitalization and schizophrenia diagnosis length were significant predictors of suicidality. At the same time, childhood adversity was not a significant predictor of suicidality.

The current research contributes to expanding mental health studies. Moreover, this study enlarges the body of knowledge in the academic world and clinical settings. It supports the disciplines of psychology, mental health and social sciences by increasing knowledge of the complicated relationships among childhood adversity, positive symptoms and suicidality.

This paper fulfills an identified need to study childhood adversity with comorbid psychiatric disorders such as schizophrenia, as well as psychiatric mental health covariates.

This research explores why tourists are dissatisfied in places considered “wonders of the world”. The authors ask if the place does not match visitors' expectations or if other factors spoil the experience.

The authors analysed the lowest-rated reviews of these wonders on TripAdvisor. The authors identified the main causes of complaints and the problems tourists faced. The authors grouped the complaints into categories and used CoDa.

The results indicate that dissatisfaction does not stem from unmet expectations regarding the monument itself, but rather from other factors related to the quality of the tourist service.

The findings of this research can be implemented in those tourist spots that, despite their global popularity, have considerable proportions of unhappy visitors, not due to the attraction itself, but to shortcomings in its administration.

This study provides a deeper insight into the causes of complaints about some of the most renowned monuments, regarded as extraordinary places, where high satisfaction levels would be anticipated. It also contributes theoretically to the literature on customer complaints in tourist places.

The COVID-19 pandemic has caused widespread disruptions to global industries, with the textile sector in South Asia being particularly hard hit. While previous studies have focused on the performance of textile sectors in individual countries, there is a gap in the literature on the comparative impact of the pandemic on the textile industry in South Asian nations. This study aims to fill this gap by investigating the performance of the textile sector in South Asian countries and identifying best practices for overcoming the pandemic’s adverse effects.

Using a comparative approach, this study analyzes the impact of COVID-19 on the performance of the textile sector in Pakistan, India and Bangladesh.

Our findings reveal that COVID-19 significantly negatively impacts the textile industry in Pakistan and India. However, Bangladesh has shown effective practices to support the textile industry and mitigate the pandemic’s adverse effects.

The findings of this study hold considerable implications for legislators, leaders, investors and supply chain management professionals operating within the South Asian textile sector. This research has the potential to inform policymakers in formulating strategies to facilitate the textile sector’s resilience during emergencies like the COVID-19 pandemic.

This paper provides significant theoretical additions to the current body of literature regarding the impact of COVID-19 on the textile sector in South Asia. The research uses the global value chain (GVC) theory as a theoretical framework to enhance understanding of the impact of global supply chains and interdependencies on the textile sector in the region.